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HEP jet assignment - Data preparation

ArXiv Matplotlib Numpy h5py Pandas Uproot tqdm llvmlite numba Docker image

Abstract

This is a repository for the jet assignment project using state-of-the-art Machine Learning method.

There is two main part in the repository, madgraph and analysis_script. The madgraph folder contains the configuration and auto-run script for generating Monte Carlo simulation data.

Madgraph

In this project, we generate the data base on the follwing model.

  1. Fully hadronic top decay[link]:
    p p > t t~ QED=0, (t > W+ b, W+ > j j), (t~ > w- b~, w- > j j )
  2. Standard Model Higgs boson produced in association with top quarks[link]:
    p p > t t~ h , (t > W+ b, W+ > j j), (t~ > w- b~, w- > j j ), (h > b b~ )
  3. Four top production(fully hadronic decay)[link]:
    p p > t t~ t t~ QED=0, (t > W+ b, W+ > j j), (t~ > w- b~, w- > j j )
  4. Semi-leptonic top decay[link]:
    p p > t t~ QED=0, (t > W+ b, W+ > j j), (t~ > W- b~, W- > l- vl~)

Analysis

The script for analysis events can be found in this folder.

The supported analysis method in this repository is:

  1. Delta R matching(truth matching)
  2. Chi-square reconstruction(Only available for two models)
  3. Cutflow
  4. Gaussian fitting for finding $\sigma$ for reconstructed invariant mass.

A full version can be found in HackMD page.

tags: Particle Physics, Machine Learning, Top quark, Transformer, SPA-Net, SPAttER

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